How to Save Utilities from a Choice Model

Introduction

This article describes how to go from a Latent Class Analysis, Hierarchical Bayes or Ensemble Choice Model output created in Displayr:

To a numeric variables containing respondent-level utilities derived from the Choice Model for each attribute level:

Requirements

A document containing a Latent Class Analysis, Hierarchical Bayes or Ensemble Choice Model output created in Displayr.

Method

1. Select the Choice Model output in your document.

2. From the toolbar menu, select Anything > Advanced Analysis > Choice Modeling > Save Variable(s) and select one of the following utilities options based on your preferred scaling:

Utilities (Mean 0) - individual-level coefficients shifted so that for each individual and attribute the mean utility across the levels is zero.
Utilities (Mean 0, Max Range 100) - individual-level coefficients scaled and shifted so that, for each individual and attribute, the lowest utility of any level is zero and the greatest utility of any level is 100.
Utilities (Mean 0, Mean Range 100) - individual-level coefficients shifted so that, for each individual and attribute, the mean utility across the levels is zero. Utilities are then all multiplied by a scaling factor per individual, so that the average range of utilities per individual across the levels of each attribute is 100.
Utilities (Min 0) - individual-level coefficients shifted so that, for each individual and attribute, the lowest utility of any level is zero.
Utilities (Min 0, Max Range 100) - individual-level coefficients shifted so that, for each individual and attribute, the mean utility across the levels is zero. Utilities are then all multiplied by a scaling factor per individual, so that the maximum range of utilities per individual across the levels of any attribute is 100.
Utilities (Min 0, Mean Range 100) - individual-level coefficients shifted so that, for each individual and attribute, the lowest utility of any level is zero. Utilities are then all multiplied by a scaling factor per individual, so that the average range of utilities per individual across the levels of each attribute is 100.

Individual-level coefficients are not able to be saved to variables for models with simulated data and models created using a CHO data file where respondent IDs were not specified.

A new variable set is added to the Data Sets tree containing the utilities for each each attribute level as shown in the Raw Data table below:

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